Deep neural networks have been widely used for feature learning in facial expression recognition systems. However, small datasets and large intra-class variability can lead to overfitting. In this paper, we propose a method which learns an optimized compact network topology for real-time facial expression recognition utilizing localized facial landmark features. Our method employs a spatio-temporal bilinear layer as backbone to capture the motion of facial landmarks during the execution of a facial expression effectively. Besides, it takes advantage of Monte Carlo Dropout to capture the model's uncertainty which is of great importance to analyze and treat uncertain cases. The performance of our method is evaluated on three widely used datasets and it is comparable to that of video-based state-of-the-art methods while it has much less complexity.

中文翻译：

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渐进时空双线性网络与 Monte Carlo Dropout 用于具有不确定性估计的基于地标的面部表情识别

深度神经网络已广泛用于面部表情识别系统中的特征学习。然而，小数据集和大的类内可变性会导致过度拟合。在本文中，我们提出了一种方法，该方法利用局部面部标志特征来学习优化的紧凑网络拓扑，用于实时面部表情识别。我们的方法采用时空双线性层作为主干，以在面部表情执行期间有效地捕捉面部标志的运动。此外，它利用Monte Carlo Dropout来捕捉模型的不确定性，这对于分析和处理不确定性案例具有重要意义。